These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

165 related articles for article (PubMed ID: 35263251)

  • 1. DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis.
    Xiang T; Song Y; Zhang C; Liu D; Chen M; Zhang F; Huang H; O'Donnell L; Cai W
    IEEE Trans Med Imaging; 2022 Aug; 41(8):2180-2190. PubMed ID: 35263251
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Weakly Supervised Deep Learning for Whole Slide Lung Cancer Image Analysis.
    Wang X; Chen H; Gan C; Lin H; Dou Q; Tsougenis E; Huang Q; Cai M; Heng PA
    IEEE Trans Cybern; 2020 Sep; 50(9):3950-3962. PubMed ID: 31484154
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Unsupervised mutual transformer learning for multi-gigapixel Whole Slide Image classification.
    Javed S; Mahmood A; Qaiser T; Werghi N; Rajpoot N
    Med Image Anal; 2024 Aug; 96():103203. PubMed ID: 38810517
    [TBL] [Abstract][Full Text] [Related]  

  • 4. LESS: Label-efficient multi-scale learning for cytological whole slide image screening.
    Zhao B; Deng W; Li ZHH; Zhou C; Gao Z; Wang G; Li X
    Med Image Anal; 2024 May; 94():103109. PubMed ID: 38387243
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Masked hypergraph learning for weakly supervised histopathology whole slide image classification.
    Shi J; Shu T; Wu K; Jiang Z; Zheng L; Wang W; Wu H; Zheng Y
    Comput Methods Programs Biomed; 2024 Aug; 253():108237. PubMed ID: 38820715
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Multi-scale representation attention based deep multiple instance learning for gigapixel whole slide image analysis.
    Xiang H; Shen J; Yan Q; Xu M; Shi X; Zhu X
    Med Image Anal; 2023 Oct; 89():102890. PubMed ID: 37467642
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Weakly supervised joint whole-slide segmentation and classification in prostate cancer.
    Pati P; Jaume G; Ayadi Z; Thandiackal K; Bozorgtabar B; Gabrani M; Goksel O
    Med Image Anal; 2023 Oct; 89():102915. PubMed ID: 37633177
    [TBL] [Abstract][Full Text] [Related]  

  • 8. MuRCL: Multi-Instance Reinforcement Contrastive Learning for Whole Slide Image Classification.
    Zhu Z; Yu L; Wu W; Yu R; Zhang D; Wang L
    IEEE Trans Med Imaging; 2023 May; 42(5):1337-1348. PubMed ID: 37015475
    [TBL] [Abstract][Full Text] [Related]  

  • 9. A universal multiple instance learning framework for whole slide image analysis.
    Zhang X; Liu C; Zhu H; Wang T; Du Z; Ding W
    Comput Biol Med; 2024 Aug; 178():108714. PubMed ID: 38889627
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Masked autoencoders with handcrafted feature predictions: Transformer for weakly supervised esophageal cancer classification.
    Bai Y; Li W; An J; Xia L; Chen H; Zhao G; Gao Z
    Comput Methods Programs Biomed; 2024 Feb; 244():107936. PubMed ID: 38016392
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Hierarchical multimodal fusion framework based on noisy label learning and attention mechanism for cancer classification with pathology and genomic features.
    Qiu L; Zhao L; Hou R; Zhao W; Zhang S; Lin Z; Teng H; Zhao J
    Comput Med Imaging Graph; 2023 Mar; 104():102176. PubMed ID: 36682215
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning.
    Li B; Li Y; Eliceiri KW
    Conf Comput Vis Pattern Recognit Workshops; 2021 Jun; 2021():14318-14328. PubMed ID: 35047230
    [TBL] [Abstract][Full Text] [Related]  

  • 13. RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval.
    Wang X; Du Y; Yang S; Zhang J; Wang M; Zhang J; Yang W; Huang J; Han X
    Med Image Anal; 2023 Jan; 83():102645. PubMed ID: 36270093
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Semi-supervised training of deep convolutional neural networks with heterogeneous data and few local annotations: An experiment on prostate histopathology image classification.
    Marini N; Otálora S; Müller H; Atzori M
    Med Image Anal; 2021 Oct; 73():102165. PubMed ID: 34303169
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Lung cancer subtype classification using histopathological images based on weakly supervised multi-instance learning.
    Zhao L; Xu X; Hou R; Zhao W; Zhong H; Teng H; Han Y; Fu X; Sun J; Zhao J
    Phys Med Biol; 2021 Dec; 66(23):. PubMed ID: 34794136
    [No Abstract]   [Full Text] [Related]  

  • 16. Enhancing Weakly Supervised Semantic Segmentation with Multi-label Contrastive Learning and LLM Features Guidance.
    Cai W; Li Y; Chen Y; Lin J; Huang Z; Gao P; Gadekallu TR; Wang W; Gao Y
    IEEE J Biomed Health Inform; 2024 Sep; PP():. PubMed ID: 39236138
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Weakly Supervised Classification for Nasopharyngeal Carcinoma with Transformer in Whole Slide Images.
    Hu Z; Wang J; Gao Q; Wu Z; Xu H; Guo Z; Quan J; Zhong L; Du M; Tong T; Chen G
    IEEE J Biomed Health Inform; 2024 Jul; PP():. PubMed ID: 38959144
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Iterative multiple instance learning for weakly annotated whole slide image classification.
    Zhou Y; Che S; Lu F; Liu S; Yan Z; Wei J; Li Y; Ding X; Lu Y
    Phys Med Biol; 2023 Jul; 68(15):. PubMed ID: 37311470
    [No Abstract]   [Full Text] [Related]  

  • 19. Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning.
    Zhou C; Jin Y; Chen Y; Huang S; Huang R; Wang Y; Zhao Y; Chen Y; Guo L; Liao J
    Comput Med Imaging Graph; 2021 Mar; 88():101861. PubMed ID: 33497891
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Deep contrastive learning based tissue clustering for annotation-free histopathology image analysis.
    Yan J; Chen H; Li X; Yao J
    Comput Med Imaging Graph; 2022 Apr; 97():102053. PubMed ID: 35306442
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.